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Article

A Novel Embedded Feature Selection and Dimensionality Reduction Method for an SVM Type Classifier to Predict Periventricular Leukomalacia (PVL) in Neonates

1
Villanova Center for Analytics of Dynamic Systems, Villanova University, 800 Lancaster Ave, Villanova, PA 19085, USA
2
June and Steve Wolfson Laboratory for Clinical and Biomedical Optics, Children’s Hospital of Philadelphia, 324 S 34th St, Philadelphia, PA 19104, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Sławomir Nowaczyk
Appl. Sci. 2021, 11(23), 11156; https://doi.org/10.3390/app112311156
Received: 15 October 2021 / Revised: 17 November 2021 / Accepted: 19 November 2021 / Published: 24 November 2021
This paper is concerned with the prediction of the occurrence of periventricular leukomalacia (PVL) in neonates after heart surgery. Our prior work shows that the Support Vector Machine (SVM) classifier can be a powerful tool in predicting clinical outcomes of such complicated and uncommon diseases, even when the number of data samples is low. In the presented work, we first illustrate and discuss the shortcomings of the traditional automatic machine learning (aML) approach. Consequently, we describe our methodology for addressing these shortcomings, while utilizing the designed interactive ML (iML) algorithm. Finally, we conclude with a discussion of the developed method and the results obtained. In sum, by adding an additional (Genetic Algorithm) optimization step in the SVM learning framework, we were able to (a) reduce the dimensionality of an SVM model from 248 to 53 features, (b) increase generalization that was confirmed by a 100% accuracy assessed on an unseen testing set, and (c) improve the overall SVM model’s performance from 65% to 100% testing accuracy, utilizing the proposed iML method. View Full-Text
Keywords: periventricular leukomalacia; active learning; interactive machine learning; support vector machine; feature selection; dimensionality reduction; congenital heart disease; pediatric periventricular leukomalacia; active learning; interactive machine learning; support vector machine; feature selection; dimensionality reduction; congenital heart disease; pediatric
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MDPI and ACS Style

Bender, D.; Licht, D.J.; Nataraj, C. A Novel Embedded Feature Selection and Dimensionality Reduction Method for an SVM Type Classifier to Predict Periventricular Leukomalacia (PVL) in Neonates. Appl. Sci. 2021, 11, 11156. https://doi.org/10.3390/app112311156

AMA Style

Bender D, Licht DJ, Nataraj C. A Novel Embedded Feature Selection and Dimensionality Reduction Method for an SVM Type Classifier to Predict Periventricular Leukomalacia (PVL) in Neonates. Applied Sciences. 2021; 11(23):11156. https://doi.org/10.3390/app112311156

Chicago/Turabian Style

Bender, Dieter, Daniel J. Licht, and C. Nataraj. 2021. "A Novel Embedded Feature Selection and Dimensionality Reduction Method for an SVM Type Classifier to Predict Periventricular Leukomalacia (PVL) in Neonates" Applied Sciences 11, no. 23: 11156. https://doi.org/10.3390/app112311156

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